Acute Coronary Syndrome (ACS) is a major cause of morbidity and mortality in the Western World. Multiple patient characteristics, including genetic constitution and metabolic status (in particular diabetes), have significant influence on patient outcome. The last decade has seen major advances in post-ACS treatment, including the administration of beta adrenergic receptor antagonists (beta blockers;BB) and angiotensin converting enzyme (ACE) inhibitors to prevent post-ACS complications. The avaiiabiiity of multiple therapeutic options with clear benefit post-ACS requires that clinical decisions must be made for individual patients. However, there are currently insufficient tools to guide the selection of post-ACS therapy in an objective and evidenced-based manner. The expansion of human genomic information has led to exciting new opportunities for the application of pharmacogenetics to improve the selection of post-ACS therapy. There is a growing list of published examples where a genetic variant in the metabolism, transport, or cellular target of a drug is clearly associated with patient toxicity or clinical outcome. The greatest obstacle to the integration of pharmacogenetics into clinical practice is the paucity of quantitative data on the predictive ability of genetics in the selection of drug therapy. Accordingly, we seek to address the need for quantitative assessment of the predictive impact of relevant genetic variants in the context of modern post-ACS therapy. Recent findings by investigators of this SCCOR proposal and others implicate genes of the PPAR gene regulatory complex as candidate modifiers of metabolic and cardiovascular disease states. This project will address the following Specific Aims: 1) To determine if a homozygous SNP genotype in PPAR complex genes (PPARalpha, PPARgamma, PGC1alpha) involved in cardiac metabolism will predict improved efficacy of ACE-I or BB therapy in preventing MACE or improving symptom score in patients with ACS;2) To perform gene-environment interaction analyses to ascertain the impact of diabetes on the predictive power of PPAR complex genes for improved efficacy of ACE-I or BB therapy in preventing MACE or improving symptom score in patients with ACS;and 3) To develop a polygenic panel of SNPs in multiple genes regulating myocardial metabolism to predict improved efficacy of ACE-I or BB in preventing MACE or improving symptom score among patients with ACS. These aims provide a comprehensive framework for our strategy to generate prospective information on the predictive power of metabolic genes on the selection of therapy for ACS. The resulting tools will allow for more precise selection of post-ACS therapy for individual patients.